Overview

Dataset statistics

Number of variables27
Number of observations91659
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.6 MiB
Average record size in memory247.1 B

Variable types

Categorical15
Numeric12

Alerts

bun_apache is highly overall correlated with creatinine_apacheHigh correlation
creatinine_apache is highly overall correlated with bun_apacheHigh correlation
gcs_motor_apache is highly overall correlated with intubated_apache and 3 other fieldsHigh correlation
apache_post_operative is highly overall correlated with elective_surgeryHigh correlation
elective_surgery is highly overall correlated with apache_post_operativeHigh correlation
cirrhosis is highly overall correlated with hepatic_failureHigh correlation
hepatic_failure is highly overall correlated with cirrhosisHigh correlation
intubated_apache is highly overall correlated with ventilated_apache and 2 other fieldsHigh correlation
ventilated_apache is highly overall correlated with intubated_apache and 2 other fieldsHigh correlation
gcs_eyes_apache is highly overall correlated with intubated_apache and 3 other fieldsHigh correlation
gcs_verbal_apache is highly overall correlated with gcs_eyes_apache and 1 other fieldsHigh correlation
diabetes_mellitus is highly overall correlated with glucose_apacheHigh correlation
glucose_apache is highly overall correlated with diabetes_mellitusHigh correlation

Reproduction

Analysis started2022-12-21 11:10:50.567710
Analysis finished2022-12-21 11:12:08.587205
Duration1 minute and 18.02 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

hospital_death
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
83747 
1
 
7912

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters91659
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 83747
91.4%
1 7912
 
8.6%

Length

2022-12-21T13:12:08.770616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-21T13:12:08.918350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 83747
91.4%
1 7912
 
8.6%

Most occurring characters

ValueCountFrequency (%)
0 83747
91.4%
1 7912
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 91659
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 83747
91.4%
1 7912
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Common 91659
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 83747
91.4%
1 7912
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 83747
91.4%
1 7912
 
8.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
73215 
1
18444 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters91659
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 73215
79.9%
1 18444
 
20.1%

Length

2022-12-21T13:12:09.054958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-21T13:12:09.172777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 73215
79.9%
1 18444
 
20.1%

Most occurring characters

ValueCountFrequency (%)
0 73215
79.9%
1 18444
 
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 91659
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 73215
79.9%
1 18444
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
Common 91659
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 73215
79.9%
1 18444
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 73215
79.9%
1 18444
 
20.1%

elective_surgery
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
74808 
1
16851 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters91659
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 74808
81.6%
1 16851
 
18.4%

Length

2022-12-21T13:12:09.285460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-21T13:12:09.489152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 74808
81.6%
1 16851
 
18.4%

Most occurring characters

ValueCountFrequency (%)
0 74808
81.6%
1 16851
 
18.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 91659
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 74808
81.6%
1 16851
 
18.4%

Most occurring scripts

ValueCountFrequency (%)
Common 91659
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 74808
81.6%
1 16851
 
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 74808
81.6%
1 16851
 
18.4%

aids
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0.0
91581 
1.0
 
78

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters274977
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 91581
99.9%
1.0 78
 
0.1%

Length

2022-12-21T13:12:09.664484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-21T13:12:09.804492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 91581
99.9%
1.0 78
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 183240
66.6%
. 91659
33.3%
1 78
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 183318
66.7%
Other Punctuation 91659
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 183240
> 99.9%
1 78
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 91659
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 274977
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 183240
66.6%
. 91659
33.3%
1 78
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 183240
66.6%
. 91659
33.3%
1 78
 
< 0.1%

cirrhosis
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0.0
90231 
1.0
 
1428

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters274977
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 90231
98.4%
1.0 1428
 
1.6%

Length

2022-12-21T13:12:09.923295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-21T13:12:10.137467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 90231
98.4%
1.0 1428
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 181890
66.1%
. 91659
33.3%
1 1428
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 183318
66.7%
Other Punctuation 91659
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 181890
99.2%
1 1428
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 91659
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 274977
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 181890
66.1%
. 91659
33.3%
1 1428
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 181890
66.1%
. 91659
33.3%
1 1428
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0.0
71167 
1.0
20492 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters274977
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 71167
77.6%
1.0 20492
 
22.4%

Length

2022-12-21T13:12:10.444723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-21T13:12:10.629960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 71167
77.6%
1.0 20492
 
22.4%

Most occurring characters

ValueCountFrequency (%)
0 162826
59.2%
. 91659
33.3%
1 20492
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 183318
66.7%
Other Punctuation 91659
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 162826
88.8%
1 20492
 
11.2%
Other Punctuation
ValueCountFrequency (%)
. 91659
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 274977
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 162826
59.2%
. 91659
33.3%
1 20492
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 162826
59.2%
. 91659
33.3%
1 20492
 
7.5%

hepatic_failure
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0.0
90477 
1.0
 
1182

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters274977
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 90477
98.7%
1.0 1182
 
1.3%

Length

2022-12-21T13:12:10.834374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-21T13:12:11.077047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 90477
98.7%
1.0 1182
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 182136
66.2%
. 91659
33.3%
1 1182
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 183318
66.7%
Other Punctuation 91659
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 182136
99.4%
1 1182
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 91659
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 274977
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 182136
66.2%
. 91659
33.3%
1 1182
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 182136
66.2%
. 91659
33.3%
1 1182
 
0.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0.0
89278 
1.0
 
2381

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters274977
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 89278
97.4%
1.0 2381
 
2.6%

Length

2022-12-21T13:12:11.219058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-21T13:12:11.413877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 89278
97.4%
1.0 2381
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 180937
65.8%
. 91659
33.3%
1 2381
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 183318
66.7%
Other Punctuation 91659
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 180937
98.7%
1 2381
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 91659
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 274977
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 180937
65.8%
. 91659
33.3%
1 2381
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 180937
65.8%
. 91659
33.3%
1 2381
 
0.9%

leukemia
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0.0
91016 
1.0
 
643

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters274977
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 91016
99.3%
1.0 643
 
0.7%

Length

2022-12-21T13:12:11.639132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-21T13:12:11.819878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 91016
99.3%
1.0 643
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 182675
66.4%
. 91659
33.3%
1 643
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 183318
66.7%
Other Punctuation 91659
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 182675
99.6%
1 643
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 91659
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 274977
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 182675
66.4%
. 91659
33.3%
1 643
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 182675
66.4%
. 91659
33.3%
1 643
 
0.2%

lymphoma
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0.0
91283 
1.0
 
376

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters274977
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 91283
99.6%
1.0 376
 
0.4%

Length

2022-12-21T13:12:11.968152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-21T13:12:12.621605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 91283
99.6%
1.0 376
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 182942
66.5%
. 91659
33.3%
1 376
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 183318
66.7%
Other Punctuation 91659
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 182942
99.8%
1 376
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 91659
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 274977
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 182942
66.5%
. 91659
33.3%
1 376
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 182942
66.5%
. 91659
33.3%
1 376
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0.0
89781 
1.0
 
1878

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters274977
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 89781
98.0%
1.0 1878
 
2.0%

Length

2022-12-21T13:12:12.814725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-21T13:12:13.008843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 89781
98.0%
1.0 1878
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 181440
66.0%
. 91659
33.3%
1 1878
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 183318
66.7%
Other Punctuation 91659
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 181440
99.0%
1 1878
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 91659
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 274977
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 181440
66.0%
. 91659
33.3%
1 1878
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 181440
66.0%
. 91659
33.3%
1 1878
 
0.7%

intubated_apache
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0.0
77898 
1.0
13761 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters274977
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 77898
85.0%
1.0 13761
 
15.0%

Length

2022-12-21T13:12:13.188483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-21T13:12:13.443010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 77898
85.0%
1.0 13761
 
15.0%

Most occurring characters

ValueCountFrequency (%)
0 169557
61.7%
. 91659
33.3%
1 13761
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 183318
66.7%
Other Punctuation 91659
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 169557
92.5%
1 13761
 
7.5%
Other Punctuation
ValueCountFrequency (%)
. 91659
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 274977
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 169557
61.7%
. 91659
33.3%
1 13761
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 169557
61.7%
. 91659
33.3%
1 13761
 
5.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0.0
62019 
1.0
29640 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters274977
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 62019
67.7%
1.0 29640
32.3%

Length

2022-12-21T13:12:13.712433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-21T13:12:13.920184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 62019
67.7%
1.0 29640
32.3%

Most occurring characters

ValueCountFrequency (%)
0 153678
55.9%
. 91659
33.3%
1 29640
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 183318
66.7%
Other Punctuation 91659
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 153678
83.8%
1 29640
 
16.2%
Other Punctuation
ValueCountFrequency (%)
. 91659
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 274977
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 153678
55.9%
. 91659
33.3%
1 29640
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 153678
55.9%
. 91659
33.3%
1 29640
 
10.8%

bun_apache
Real number (ℝ)

Distinct19675
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.748069
Minimum4
Maximum127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-21T13:12:14.253501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q114
median22
Q329.479939
95-th percentile63
Maximum127
Range123
Interquartile range (IQR)15.479939

Descriptive statistics

Standard deviation18.500936
Coefficient of variation (CV)0.71853686
Kurtosis7.4293565
Mean25.748069
Median Absolute Deviation (MAD)8
Skewness2.3663893
Sum2360042.2
Variance342.28464
MonotonicityNot monotonic
2022-12-21T13:12:14.606318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 3186
 
3.5%
14 3173
 
3.5%
12 3131
 
3.4%
15 3092
 
3.4%
11 3023
 
3.3%
16 2853
 
3.1%
10 2797
 
3.1%
17 2602
 
2.8%
18 2435
 
2.7%
9 2354
 
2.6%
Other values (19665) 63013
68.7%
ValueCountFrequency (%)
4 776
0.8%
4.3 2
 
< 0.1%
4.4 1
 
< 0.1%
4.6 2
 
< 0.1%
4.7 1
 
< 0.1%
4.8 1
 
< 0.1%
5 744
0.8%
5.1 1
 
< 0.1%
5.3 3
 
< 0.1%
5.4 3
 
< 0.1%
ValueCountFrequency (%)
127 355
0.4%
126 18
 
< 0.1%
125 13
 
< 0.1%
124 15
 
< 0.1%
123 21
 
< 0.1%
122.2 1
 
< 0.1%
122 13
 
< 0.1%
121 27
 
< 0.1%
120 17
 
< 0.1%
119 19
 
< 0.1%

creatinine_apache
Real number (ℝ)

Distinct19917
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4800012
Minimum0.079365237
Maximum11.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-21T13:12:14.962581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.079365237
5-th percentile0.51
Q10.79
median1.1799503
Q31.5652657
95-th percentile3.81
Maximum11.18
Range11.100635
Interquartile range (IQR)0.7752657

Descriptive statistics

Standard deviation1.3658352
Coefficient of variation (CV)0.92286082
Kurtosis19.457096
Mean1.4800012
Median Absolute Deviation (MAD)0.38995032
Skewness3.9371285
Sum135655.43
Variance1.8655057
MonotonicityNot monotonic
2022-12-21T13:12:15.225797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 2568
 
2.8%
0.7 2552
 
2.8%
0.9 2058
 
2.2%
0.6 1906
 
2.1%
1.1 1329
 
1.4%
1 1328
 
1.4%
1.2 1165
 
1.3%
0.5 1121
 
1.2%
1.3 961
 
1.0%
1.4 796
 
0.9%
Other values (19907) 75875
82.8%
ValueCountFrequency (%)
0.07936523664 1
 
< 0.1%
0.1857121653 1
 
< 0.1%
0.2653857675 1
 
< 0.1%
0.2911592128 1
 
< 0.1%
0.3 361
0.4%
0.3021057416 1
 
< 0.1%
0.3079817649 1
 
< 0.1%
0.3094222677 1
 
< 0.1%
0.31 40
 
< 0.1%
0.316 2
 
< 0.1%
ValueCountFrequency (%)
11.18 349
0.4%
11.17 2
 
< 0.1%
11.11 1
 
< 0.1%
11.1 4
 
< 0.1%
11.08 1
 
< 0.1%
11.07 1
 
< 0.1%
11.05 1
 
< 0.1%
11.03 1
 
< 0.1%
11 7
 
< 0.1%
10.97 1
 
< 0.1%

gcs_eyes_apache
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
4.0
62998 
3.0
15604 
1.0
8274 
2.0
 
4783

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters274977
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.0 62998
68.7%
3.0 15604
 
17.0%
1.0 8274
 
9.0%
2.0 4783
 
5.2%

Length

2022-12-21T13:12:15.488192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-21T13:12:15.677970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0 62998
68.7%
3.0 15604
 
17.0%
1.0 8274
 
9.0%
2.0 4783
 
5.2%

Most occurring characters

ValueCountFrequency (%)
. 91659
33.3%
0 91659
33.3%
4 62998
22.9%
3 15604
 
5.7%
1 8274
 
3.0%
2 4783
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 183318
66.7%
Other Punctuation 91659
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91659
50.0%
4 62998
34.4%
3 15604
 
8.5%
1 8274
 
4.5%
2 4783
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 91659
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 274977
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 91659
33.3%
0 91659
33.3%
4 62998
22.9%
3 15604
 
5.7%
1 8274
 
3.0%
2 4783
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 91659
33.3%
0 91659
33.3%
4 62998
22.9%
3 15604
 
5.7%
1 8274
 
3.0%
2 4783
 
1.7%

gcs_motor_apache
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4599876
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-21T13:12:15.825569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median6
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2788468
Coefficient of variation (CV)0.23422156
Kurtosis6.3211196
Mean5.4599876
Median Absolute Deviation (MAD)0
Skewness-2.6963216
Sum500457
Variance1.6354492
MonotonicityNot monotonic
2022-12-21T13:12:15.981847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 71000
77.5%
5 9612
 
10.5%
1 5543
 
6.0%
4 4651
 
5.1%
3 544
 
0.6%
2 309
 
0.3%
ValueCountFrequency (%)
1 5543
 
6.0%
2 309
 
0.3%
3 544
 
0.6%
4 4651
 
5.1%
5 9612
 
10.5%
6 71000
77.5%
ValueCountFrequency (%)
6 71000
77.5%
5 9612
 
10.5%
4 4651
 
5.1%
3 544
 
0.6%
2 309
 
0.3%
1 5543
 
6.0%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
5.0
56911 
1.0
16741 
4.0
11631 
3.0
 
4380
2.0
 
1996

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters274977
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row5.0
4th row5.0
5th row4.0

Common Values

ValueCountFrequency (%)
5.0 56911
62.1%
1.0 16741
 
18.3%
4.0 11631
 
12.7%
3.0 4380
 
4.8%
2.0 1996
 
2.2%

Length

2022-12-21T13:12:16.190307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-21T13:12:16.377900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0 56911
62.1%
1.0 16741
 
18.3%
4.0 11631
 
12.7%
3.0 4380
 
4.8%
2.0 1996
 
2.2%

Most occurring characters

ValueCountFrequency (%)
. 91659
33.3%
0 91659
33.3%
5 56911
20.7%
1 16741
 
6.1%
4 11631
 
4.2%
3 4380
 
1.6%
2 1996
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 183318
66.7%
Other Punctuation 91659
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91659
50.0%
5 56911
31.0%
1 16741
 
9.1%
4 11631
 
6.3%
3 4380
 
2.4%
2 1996
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 91659
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 274977
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 91659
33.3%
0 91659
33.3%
5 56911
20.7%
1 16741
 
6.1%
4 11631
 
4.2%
3 4380
 
1.6%
2 1996
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 91659
33.3%
0 91659
33.3%
5 56911
20.7%
1 16741
 
6.1%
4 11631
 
4.2%
3 4380
 
1.6%
2 1996
 
0.7%

glucose_apache
Real number (ℝ)

Distinct11538
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.80854
Minimum39
Maximum598.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-21T13:12:16.556699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile75
Q1100
median144.27727
Q3187
95-th percentile328
Maximum598.7
Range559.7
Interquartile range (IQR)87

Descriptive statistics

Standard deviation85.351729
Coefficient of variation (CV)0.53408741
Kurtosis5.4207852
Mean159.80854
Median Absolute Deviation (MAD)44.277269
Skewness1.9697907
Sum14647891
Variance7284.9177
MonotonicityNot monotonic
2022-12-21T13:12:16.730326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96 973
 
1.1%
92 955
 
1.0%
97 930
 
1.0%
98 917
 
1.0%
93 916
 
1.0%
91 916
 
1.0%
95 906
 
1.0%
99 901
 
1.0%
94 899
 
1.0%
102 861
 
0.9%
Other values (11528) 82485
90.0%
ValueCountFrequency (%)
39 409
0.4%
40 31
 
< 0.1%
41 42
 
< 0.1%
42 48
 
0.1%
43 33
 
< 0.1%
44 22
 
< 0.1%
45 32
 
< 0.1%
46 44
 
< 0.1%
47 51
 
0.1%
48 50
 
0.1%
ValueCountFrequency (%)
598.7 408
0.4%
598 7
 
< 0.1%
597 3
 
< 0.1%
596 2
 
< 0.1%
595 3
 
< 0.1%
594 4
 
< 0.1%
593 2
 
< 0.1%
592 3
 
< 0.1%
591 4
 
< 0.1%
590 2
 
< 0.1%

heart_rate_apache
Real number (ℝ)

Distinct964
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.705769
Minimum30
Maximum178
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-21T13:12:16.916735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile47
Q187
median104
Q3120
95-th percentile146
Maximum178
Range148
Interquartile range (IQR)33

Descriptive statistics

Standard deviation30.732672
Coefficient of variation (CV)0.30823363
Kurtosis-0.42576079
Mean99.705769
Median Absolute Deviation (MAD)16
Skewness-0.26825823
Sum9138931.1
Variance944.4971
MonotonicityNot monotonic
2022-12-21T13:12:17.157848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1860
 
2.0%
108 1806
 
2.0%
102 1794
 
2.0%
104 1724
 
1.9%
98 1716
 
1.9%
106 1689
 
1.8%
110 1638
 
1.8%
96 1634
 
1.8%
60 1624
 
1.8%
112 1609
 
1.8%
Other values (954) 74565
81.4%
ValueCountFrequency (%)
30 561
0.6%
31 73
 
0.1%
32 105
 
0.1%
33 79
 
0.1%
34 111
 
0.1%
35 108
 
0.1%
36 127
 
0.1%
37 130
 
0.1%
38 171
 
0.2%
39 175
 
0.2%
ValueCountFrequency (%)
178 462
0.5%
177 33
 
< 0.1%
176 37
 
< 0.1%
175 46
 
0.1%
174 57
 
0.1%
173 38
 
< 0.1%
172 56
 
0.1%
171 35
 
< 0.1%
170 70
 
0.1%
169 64
 
0.1%

hematocrit_apache
Real number (ℝ)

Distinct20168
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.042069
Minimum16.2
Maximum51.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-21T13:12:17.392094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum16.2
5-th percentile22.3
Q129.5
median33.214529
Q336.6
95-th percentile43.1
Maximum51.4
Range35.2
Interquartile range (IQR)7.1

Descriptive statistics

Standard deviation6.1325845
Coefficient of variation (CV)0.18559929
Kurtosis0.27194783
Mean33.042069
Median Absolute Deviation (MAD)3.5854711
Skewness-0.028107903
Sum3028603
Variance37.608592
MonotonicityNot monotonic
2022-12-21T13:12:17.606413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 544
 
0.6%
36 538
 
0.6%
35 523
 
0.6%
33 504
 
0.5%
31 494
 
0.5%
29 477
 
0.5%
32 477
 
0.5%
37 460
 
0.5%
30 458
 
0.5%
38 445
 
0.5%
Other values (20158) 86739
94.6%
ValueCountFrequency (%)
16.2 380
0.4%
16.3 18
 
< 0.1%
16.4 20
 
< 0.1%
16.5 21
 
< 0.1%
16.6 8
 
< 0.1%
16.7 15
 
< 0.1%
16.8 22
 
< 0.1%
16.9 21
 
< 0.1%
17 35
 
< 0.1%
17.1 22
 
< 0.1%
ValueCountFrequency (%)
51.4 368
0.4%
51.3 17
 
< 0.1%
51.2 11
 
< 0.1%
51.1 11
 
< 0.1%
51 16
 
< 0.1%
50.9 13
 
< 0.1%
50.8 13
 
< 0.1%
50.7 11
 
< 0.1%
50.6 15
 
< 0.1%
50.5 13
 
< 0.1%

map_apache
Real number (ℝ)

Distinct1092
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.016055
Minimum40
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-21T13:12:17.871670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile42
Q154
median67
Q3124
95-th percentile164
Maximum200
Range160
Interquartile range (IQR)70

Descriptive statistics

Standard deviation41.818392
Coefficient of variation (CV)0.47512232
Kurtosis-0.77132126
Mean88.016055
Median Absolute Deviation (MAD)21
Skewness0.70181204
Sum8067463.6
Variance1748.7779
MonotonicityNot monotonic
2022-12-21T13:12:18.253087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 2122
 
2.3%
54 2029
 
2.2%
60 1971
 
2.2%
58 1955
 
2.1%
53 1893
 
2.1%
55 1882
 
2.1%
52 1877
 
2.0%
40 1874
 
2.0%
57 1844
 
2.0%
51 1814
 
2.0%
Other values (1082) 72398
79.0%
ValueCountFrequency (%)
40 1874
2.0%
41 1450
1.6%
42 1383
1.5%
43 1358
1.5%
44 1360
1.5%
45 1333
1.5%
46 1435
1.6%
47 1507
1.6%
48 1654
1.8%
49 1579
1.7%
ValueCountFrequency (%)
200 141
0.2%
199 128
0.1%
198 100
0.1%
197 113
0.1%
196 121
0.1%
195 121
0.1%
194 109
0.1%
193 90
0.1%
192 109
0.1%
191 95
0.1%

resprate_apache
Real number (ℝ)

Distinct1245
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.808423
Minimum4
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-21T13:12:18.628348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q111
median28
Q336
95-th percentile53
Maximum60
Range56
Interquartile range (IQR)25

Descriptive statistics

Standard deviation15.010394
Coefficient of variation (CV)0.58160832
Kurtosis-0.90134954
Mean25.808423
Median Absolute Deviation (MAD)14
Skewness0.26058631
Sum2365574.3
Variance225.31192
MonotonicityNot monotonic
2022-12-21T13:12:18.899758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 4303
 
4.7%
12 4221
 
4.6%
11 3911
 
4.3%
4 3528
 
3.8%
9 3432
 
3.7%
30 3145
 
3.4%
28 3074
 
3.4%
8 2927
 
3.2%
29 2887
 
3.1%
31 2725
 
3.0%
Other values (1235) 57506
62.7%
ValueCountFrequency (%)
4 3528
3.8%
5 2076
2.3%
5.9 1
 
< 0.1%
6 2091
2.3%
7 2415
2.6%
7.1 2
 
< 0.1%
7.2 1
 
< 0.1%
7.8 1
 
< 0.1%
8 2927
3.2%
8.4 1
 
< 0.1%
ValueCountFrequency (%)
60 937
1.0%
59 666
0.7%
58 516
0.6%
57 509
0.6%
56 494
0.5%
55 511
0.6%
54 512
0.6%
53 524
0.6%
52 569
0.6%
51 554
0.6%

sodium_apache
Real number (ℝ)

Distinct18656
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.93389
Minimum117
Maximum158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-21T13:12:19.270125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum117
5-th percentile130
Q1136
median138
Q3140
95-th percentile145
Maximum158
Range41
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.7269919
Coefficient of variation (CV)0.034269983
Kurtosis4.3328398
Mean137.93389
Median Absolute Deviation (MAD)2
Skewness-0.30454821
Sum12642882
Variance22.344453
MonotonicityNot monotonic
2022-12-21T13:12:19.504853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
139 7543
 
8.2%
138 7461
 
8.1%
140 6948
 
7.6%
137 6741
 
7.4%
136 5766
 
6.3%
141 5636
 
6.1%
135 4481
 
4.9%
142 4349
 
4.7%
134 3660
 
4.0%
143 2858
 
3.1%
Other values (18646) 36216
39.5%
ValueCountFrequency (%)
117 470
0.5%
118 62
 
0.1%
119 87
 
0.1%
120 83
 
0.1%
120.4 1
 
< 0.1%
121 119
 
0.1%
122 150
 
0.2%
122.4 1
 
< 0.1%
123 162
 
0.2%
123.4 1
 
< 0.1%
ValueCountFrequency (%)
158 314
0.3%
157 58
 
0.1%
156 62
 
0.1%
155 104
 
0.1%
154 95
 
0.1%
153 141
0.2%
152 190
0.2%
151.1 1
 
< 0.1%
151 209
0.2%
150 334
0.4%

temp_apache
Real number (ℝ)

Distinct4236
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.414908
Minimum32.1
Maximum39.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-21T13:12:19.758642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum32.1
5-th percentile35.3
Q136.2
median36.5
Q336.7
95-th percentile37.3
Maximum39.7
Range7.6
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.81589022
Coefficient of variation (CV)0.02240539
Kurtosis9.4780535
Mean36.414908
Median Absolute Deviation (MAD)0.3
Skewness-0.9870833
Sum3337754.1
Variance0.66567685
MonotonicityNot monotonic
2022-12-21T13:12:20.030501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.4 9347
 
10.2%
36.6 8572
 
9.4%
36.7 8076
 
8.8%
36.3 6667
 
7.3%
36.5 6193
 
6.8%
36.8 5778
 
6.3%
36.2 4802
 
5.2%
36.1 4594
 
5.0%
36.9 3698
 
4.0%
36 2807
 
3.1%
Other values (4226) 31125
34.0%
ValueCountFrequency (%)
32.1 516
0.6%
32.16 1
 
< 0.1%
32.2 62
 
0.1%
32.22 1
 
< 0.1%
32.27 1
 
< 0.1%
32.3 53
 
0.1%
32.33 1
 
< 0.1%
32.4 49
 
0.1%
32.5 56
 
0.1%
32.6 70
 
0.1%
ValueCountFrequency (%)
39.7 370
0.4%
39.66 4
 
< 0.1%
39.61 5
 
< 0.1%
39.6 95
 
0.1%
39.55 9
 
< 0.1%
39.5 132
 
0.1%
39.44 9
 
< 0.1%
39.4 211
0.2%
39.39 1
 
< 0.1%
39.38 9
 
< 0.1%

wbc_apache
Real number (ℝ)

Distinct25024
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.073749
Minimum0.9
Maximum45.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-21T13:12:20.283687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile4.8
Q18.34
median11.2
Q314.060112
95-th percentile23.4
Maximum45.8
Range44.9
Interquartile range (IQR)5.720112

Descriptive statistics

Standard deviation6.0866763
Coefficient of variation (CV)0.5041248
Kurtosis6.2412141
Mean12.073749
Median Absolute Deviation (MAD)2.86
Skewness1.9044326
Sum1106667.7
Variance37.047628
MonotonicityNot monotonic
2022-12-21T13:12:20.501810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.4 546
 
0.6%
8 540
 
0.6%
8.4 537
 
0.6%
7.6 533
 
0.6%
8.8 527
 
0.6%
7.8 525
 
0.6%
8.2 523
 
0.6%
7.9 520
 
0.6%
7 511
 
0.6%
9 511
 
0.6%
Other values (25014) 86386
94.2%
ValueCountFrequency (%)
0.9 341
0.4%
0.92 1
 
< 0.1%
0.93 1
 
< 0.1%
0.94 1
 
< 0.1%
0.95 1
 
< 0.1%
0.97 2
 
< 0.1%
1 38
 
< 0.1%
1.03 1
 
< 0.1%
1.05 3
 
< 0.1%
1.06 1
 
< 0.1%
ValueCountFrequency (%)
45.8 347
0.4%
45.7 2
 
< 0.1%
45.68 2
 
< 0.1%
45.6 1
 
< 0.1%
45.5 2
 
< 0.1%
45.49 1
 
< 0.1%
45.4 4
 
< 0.1%
45.35 1
 
< 0.1%
45.3 3
 
< 0.1%
45.2 1
 
< 0.1%

bmi
Real number (ℝ)

Distinct38273
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.190047
Minimum14.844926
Maximum67.81499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-21T13:12:20.750417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum14.844926
5-th percentile18.934657
Q123.784833
median27.903396
Q332.690378
95-th percentile44.256442
Maximum67.81499
Range52.970064
Interquartile range (IQR)8.9055446

Descriptive statistics

Standard deviation8.1239492
Coefficient of variation (CV)0.2783123
Kurtosis3.6337389
Mean29.190047
Median Absolute Deviation (MAD)4.3686555
Skewness1.4631468
Sum2675530.5
Variance65.998551
MonotonicityNot monotonic
2022-12-21T13:12:20.973339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.84492591 440
 
0.5%
67.81498973 420
 
0.5%
24.01776785 87
 
0.1%
24.20811 86
 
0.1%
27.35933163 82
 
0.1%
22.60610272 79
 
0.1%
23.29590458 79
 
0.1%
24.8046875 78
 
0.1%
25.81229652 77
 
0.1%
29.049732 76
 
0.1%
Other values (38263) 90155
98.4%
ValueCountFrequency (%)
14.84492591 440
0.5%
14.84526746 1
 
< 0.1%
14.86419531 1
 
< 0.1%
14.86453979 2
 
< 0.1%
14.86695021 1
 
< 0.1%
14.86737667 1
 
< 0.1%
14.87603306 1
 
< 0.1%
14.8780004 1
 
< 0.1%
14.87871348 1
 
< 0.1%
14.88002976 1
 
< 0.1%
ValueCountFrequency (%)
67.81498973 420
0.5%
67.81263563 1
 
< 0.1%
67.7978059 1
 
< 0.1%
67.78345128 1
 
< 0.1%
67.77927558 1
 
< 0.1%
67.74710784 1
 
< 0.1%
67.72486772 1
 
< 0.1%
67.71626298 1
 
< 0.1%
67.71150768 1
 
< 0.1%
67.5078125 1
 
< 0.1%

Interactions

2022-12-21T13:12:03.979588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:32.708887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:35.418694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:38.042122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:40.672378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:43.272351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:45.881707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:48.736950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:51.374080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:54.345665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:57.916240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:01.317595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:04.179026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:32.982026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:35.626248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:38.253562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:40.864552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:43.497913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:46.107547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:48.954212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:51.656022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:54.693835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:58.215769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:01.519761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:04.360739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:33.204949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:35.823655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:38.459049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:41.044942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:43.694538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:46.304771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:49.160939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:51.848442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:55.044034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:58.426055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:01.704011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:04.547500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:33.487430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:36.017847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:38.652946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:41.253824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:43.894711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:46.496292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:49.348073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:52.152291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:55.412973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:58.917280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:01.885028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:04.785829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:33.723871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:36.220008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:38.856998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:41.459376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:44.099860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:46.711340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:49.547521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:52.412951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:55.755632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:59.128916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:02.106095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:05.027751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:33.952403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:36.436894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:39.064222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:41.767327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:44.312598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:47.018461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:49.771731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:52.633469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:56.050643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:59.358478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:02.401856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:05.237178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:34.172753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:36.665175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:39.480405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:42.009471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:44.520128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:47.430476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:49.997226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:52.870000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:56.355018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:59.627576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:02.594447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:05.477789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:34.406361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:36.875194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:39.707547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:42.231204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:44.738937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:47.628713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:50.221887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:53.158009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:56.706109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:59.907571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:02.799821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:05.670981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:34.611931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:37.113201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:39.902530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:42.453237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:44.958424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:47.846717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:50.438846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:53.383744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:57.062673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:00.175564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:03.068725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:05.888749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:34.822205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:37.380828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:40.093109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:42.635919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:45.154521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:48.079543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:50.679131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:53.598607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:57.250799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:00.451656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:03.323834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:06.110670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:35.032936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:37.608820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:40.294959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:42.852485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:45.360759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:48.310627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:50.965406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:53.807303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:57.458513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:00.732311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:03.589835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:06.326326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:35.223570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:37.858118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:40.476432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:43.069200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:45.584187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:48.522516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:51.163983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:54.017698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:11:57.716473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:01.042209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-21T13:12:03.801834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-12-21T13:12:21.203583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-21T13:12:22.338839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-21T13:12:23.365638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-21T13:12:24.329917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-21T13:12:25.187851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-21T13:12:25.726206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-21T13:12:06.676378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-21T13:12:07.829049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

hospital_deathapache_post_operativeelective_surgeryaidscirrhosisdiabetes_mellitushepatic_failureimmunosuppressionleukemialymphomasolid_tumor_with_metastasisintubated_apacheventilated_apachebun_apachecreatinine_apachegcs_eyes_apachegcs_motor_apachegcs_verbal_apacheglucose_apacheheart_rate_apachehematocrit_apachemap_apacheresprate_apachesodium_apachetemp_apachewbc_apachebmi
00000.00.01.00.00.00.00.00.00.00.031.0000002.5100003.06.04.0168.000000118.027.40000040.036.0134.00000039.314.10000022.730000
10000.00.01.00.00.00.00.00.00.01.09.0000000.5600001.03.01.0145.000000120.036.90000046.033.0145.00000035.112.70000027.420000
20000.00.00.00.00.00.00.00.00.00.026.5499011.5401113.06.05.0161.838925102.033.18377468.037.0137.28881836.712.08002231.950000
30110.00.00.00.00.00.00.00.01.01.029.9066911.6715054.06.05.0185.000000114.025.90000060.04.0137.60481834.88.00000022.640000
40000.00.00.00.00.00.00.00.00.00.022.9391651.3926133.05.04.0144.64980560.034.372624103.016.0138.05437936.710.23422329.779233
50000.00.01.00.00.00.00.00.00.00.013.0000000.7100004.06.05.0156.000000113.044.200000130.035.0137.00000036.610.90000027.560000
60000.00.01.00.00.00.00.00.01.01.018.0000000.7800004.06.05.0197.000000133.033.500000138.053.0135.00000035.05.90000057.450000
70000.00.00.00.01.00.00.00.00.01.048.0000002.0500004.06.05.0164.000000120.022.60000060.028.0140.00000036.612.80000027.882868
81000.00.00.00.00.00.00.00.00.01.015.0000001.1600004.06.05.0380.00000082.037.90000066.014.0142.00000036.924.70000032.564259
90000.00.00.00.00.00.00.00.00.00.010.0000000.8300004.06.05.0134.00000094.037.20000058.046.0139.00000036.38.40000025.710000
hospital_deathapache_post_operativeelective_surgeryaidscirrhosisdiabetes_mellitushepatic_failureimmunosuppressionleukemialymphomasolid_tumor_with_metastasisintubated_apacheventilated_apachebun_apachecreatinine_apachegcs_eyes_apachegcs_motor_apachegcs_verbal_apacheglucose_apacheheart_rate_apachehematocrit_apachemap_apacheresprate_apachesodium_apachetemp_apachewbc_apachebmi
917030000.00.01.00.00.00.00.00.00.00.024.0000001.4000003.06.05.0198.00000060.0000036.000000133.00000034.000000136.00000036.9000015.53000045.935203
917040000.00.00.00.00.00.00.00.00.00.050.0000007.1000004.06.04.0251.000000106.0000035.00000094.00000014.000000140.00000036.800008.56000032.992923
917050000.00.01.00.00.00.00.00.00.00.015.0000000.8000004.06.05.0240.00000088.0000033.50685954.00000013.000000134.00000036.4000011.91269728.876843
917060000.00.00.00.00.00.00.00.00.00.015.0000000.7000004.06.05.091.00000055.0000041.00000062.00000012.000000139.00000036.600007.14000019.770448
917070000.00.00.00.00.00.00.00.00.00.026.3546391.5430243.05.04.0165.29027599.3271233.44471889.41213725.824444137.86923736.4337312.19732033.933518
917080000.00.01.00.00.00.00.01.00.01.030.4516761.4934224.06.05.0381.000000115.0000031.64109548.0000009.000000136.25125236.6000014.22016623.060250
917090000.00.00.00.00.00.00.00.00.00.034.0000002.3000004.06.05.0177.000000100.0000033.00000062.00000033.000000136.00000037.400004.22000047.179671
917100000.00.01.00.00.00.00.00.00.00.033.0000002.3000003.06.04.0538.000000158.0000036.00000057.0000004.000000135.00000035.8000017.55000027.236914
917110000.00.00.00.00.00.00.00.00.00.025.0359061.3710664.05.04.0140.04797960.0000032.44689754.00000014.000000138.28820436.3000010.67526323.297481
917120110.00.00.00.00.00.00.00.00.01.027.0000001.5000004.06.01.0158.000000101.0000036.00000056.0000004.000000132.00000036.0000024.40000022.031250